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benchmark 0.1.5
benchmark is a Python benchmarking framework, similar to Steve Purcell’s
unittest in basic structure. It’s as simple as:
import benchmark
import math
class Benchmark_Sqrt(benchmark.Benchmark):
each = 100 # allows for differing number of runs
def setUp(self):
# Only using setUp in order to subclass later
# Can also specify tearDown, eachSetUp, and eachTearDown
self.size = 25000
def test_pow_operator(self):
for i in xrange(self.size):
z = i**.5
def test_pow_function(self):
for i in xrange(self.size):
z = pow(i, .5)
def test_sqrt_function(self):
for i in xrange(self.size):
z = math.sqrt(i)
class Benchmark_Sqrt2(Benchmark_Sqrt):
# Subclass the previous benchmark to change input using
# self.setUp()
label = "Benchmark Sqrt on a larger range"
# The above label comes from the class name, this oen
# comes from the label attribute
each = 50
def setUp(self):
self.size = 750000
if __name__ == '__main__':
benchmark.main(format="markdown", numberFormat="%.4g")
# could have written benchmark.main(each=50) if the
# first class shouldn't have been run 100 times.
which produces:
Benchmark Report
================
Benchmark Sqrt
--------------
name | rank | runs | mean | sd | timesBaseline
--------------|------|------|----------|-----------|--------------
pow operator | 1 | 100 | 0.004163 | 0.0001469 | 1.0
sqrt function | 2 | 100 | 0.004584 | 0.000379 | 1.10126499627
pow function | 3 | 100 | 0.005496 | 0.0002709 | 1.32031909568
Benchmark Sqrt on a larger range
--------------------------------
name | rank | runs | mean | sd | timesBaseline
--------------|------|------|--------|----------|--------------
pow operator | 1 | 50 | 0.1265 | 0.004772 | 1.0
sqrt function | 2 | 50 | 0.1375 | 0.004729 | 1.08705384
pow function | 3 | 50 | 0.1649 | 0.003935 | 1.30357975885
Each of the above 450 runs were run in random, non-consecutive order by
`benchmark` v0.1.4 (http://jspi.es/benchmark) with Python 2.7.1
Darwin-11.3.0-x86_64 on 2012-04-17 19:43:01.
More examples are available in the example folder or visit
http://jspi.es/benchmark for more information.
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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